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Media Mix Modelling generally includes only media channels as factors influencing the target KPI (e.g. sales), while Marketing Mix Modelling includes a broader set of additional non-media factors such as pricing / discounting. So you can think of Media Mix Modelling as a narrower version of Marketing Mix Modelling.
MMM is gaining a lot of traction especially with digital brands because of several factors:
MMM works at the level of marketing channels – e.g. Facebook Ads or TikTok or Youtube or Google Ads PMax could be these channels. Depending on your marketing/media mix, you may want to divide larger platforms or media types into subcategories – e.g. split FB Ads or Google Ads into several buckets by campaign type (by targeting, objective, bidding type or creative execution etc), or TV by specific networks and so on.
In total it is usually realistic to work with 6-25 channels in total – it depends on how many channels you have, what their shares are, how many data points you have and other factors. Designing a good channel structure (channel grouping) is one of the important phases of MMM implementation and requires some experience with what is ok vs what can cause problems for MMM.
MMM is not a suitable method for determining ROI on very granular level – such as keyword or ad-group.
No, you don’t install any pixel or any other user tracking – MMM works with aggregate data (e.g. total daily sales and daily spends on Google Pmax, FB ASC, Youtube video ads etc).
It identifies patterns in this data using statistical learning and this way it can quantify the impact of each of the explaining variables (such as spend on Google Pmax) on the explained variable (total sales). In a super-simplified way for illustrative purposes think of MMM as investigating “In the 38.week of last year there was an increase in spend on PMax, is it possible to identify some increase in total sales in W38, W39 etc that can be tied to the change?”
You don’t need any pixel or user tracking for this, you can learn this from the daily time series of PMax costs and other channel costs and total sales – MMM does this, of course in a much more sophisticated way but the principle remains – it investigates daily or weekly time series and using statistical learning algorithms it identifies what channel drives what results.
The initial implementation usually takes a few weeks and consists of
It is important to understand that the more you as the advertiser/business owner are involved the better – some vendors offer a “fully automated no-touch” approach but in our experience this typically results in models that may be mathematically ok but just don’t make business sense and nobody uses them afterwards.
The initial implementation usually takes a few weeks – while it is easy to “get some model” within days, it typically takes quite a few iterations (even with the most advanced AI automation) to arrive at a production-level model. So you should expect first useful insights in a matter of weeks.
MMM is best treated as an iterative process – the model should not be a static thing developed once but rather a continuous iterative process – with new calibration points and new data the model is refined and improved. In our experience:
Generally you will need 2-3 years of daily or weekly data for:
It is important that there is some variance in the volume of the channel activity – eg. sometimes you run more FB ads, sometimes less. In most cases this is not an issue but for example having a football league sponsorship for the whole investigated period would not be a good use case for MMM measurement as there is no variance in it.
There are multiple options how to collect and integrate the data to MMM:
The data needs to be structured into channels (typically 6-25 channels) – the specific channel grouping used for MMM should be both suitable for the modelling process and useful for you as a marketer (i.e. it should reflect how you budget and manage marketing). This is often not easy and there will be tradeoffs to be made – good channel structure is a very important factor in MMM success and is a part where significant previous experience with MMM and marketing is indispensable.
From a business perspective both attribution model and MMM try to answer the same (*) question: How much did marketing channel X drive sales?
Is not very good at measuring completely new channels or very small channels.
(*) technically there are differences between what attribution and MMM measure (and not just how) and this is a good topic for analysts but from a marketing manager’s position the purpose and the main business question is the same.
MMM and attribution are not mutually exclusive but rather complementary – MMM is better for strategic budgeting decisions (think weekly, monthly, quarterly horizon) and for measuring channels that are typically underrepresented even by data driven attribution models (video, influencers etc) or cannot be measured by online attribution at all (TV advertising, effect of discounting etc). It also benefits from being completely resistant to user tracking issues (as it does not rely on any user-level data).
Attribution on the other hand is more suitable for operational measurement in online marketing (think daily and weekly horizon). Advanced advertisers use a triangle of measurement methods:
If you are a smaller or starting brand (spending less than 2m USD a year on marketing), using attribution may be completely sufficient for your needs.
Get serious about incrementality and testing. Get used to doing experiments in marketing regularly. Explain marketing incrementality to senior leadership in your company – CEO, CFO etc. Explain the limitations of attribution models.
Existing experiment results can significantly speed up any MMM program and will almost certainly lead to a faster value and ROI on your MMM project.
Set up a good data collection process for areas like promotions (promotion calendars and plans), main events affecting your business, discounting or major pricing changes etc.
We recommend refreshing data on a weekly basis – it is possible to refresh even daily and for some advertisers it makes sense, but on the whole weekly refreshes is a good default.
What refreshing means: not only ingesting new data (spends for past days, week) but also the model updates itself (“learns on new data”).
For using MMM you don’t need to understand statistics – MMM results should be easy to interpret by business users (if they are not, it is a sign of a low-quality model and/or its vendor).
For developing MMM you (or your team or external vendor) need to understand statistical learning but also – and this is sometimes underestimated by general data scientists trying to develop MMMs on their own from scratch – experience.
You don’t. What your organization needs is some maturity in working with data and measurement – e.g. understanding the concept of incrementality or being comfortable with working with uncertainty. You should also see that there is a need for marketing measurement and how it can impact resource and budget decisions in your organization.
MMM results are of course often used by analysts but also (or even primarily) by senior people in the marketing department and other departments like finance who are responsible for budgeting, planning and revenue delivery.
In principle yes and some companies do it – esp.those with strong inhouse data science teams and previous experience with MMM. There are even some open-source libraries that can help you – however beware that there is a huge difference between “getting a first-attempt model using some open-source library” and “production-useful MMM” – there is a high chance that your first models will be useless at best, highly dangerous for your business at worst.
Another aspect that we often see underestimated by inhouse attempts at MMM is continuous development and support: to actually get the benefits of using MMM, you should think of MMM as an iterative process – you develop a model, you use it, gather feedback, get new calibration points, then you improve the model etc. so if you are thinking about inhouse MMM development, you should commit the data science/data resources for the long term, not just for initial development.
Generally all marketing channels as long as you have a time series (ideally daily or weekly) of their costs and/or other measure of their volume – impressions, GRPs etc.
So for example Google Ads (often split into multiple sub-channels based on campaign or objective type), FB/Meta Ads (also often split into multiple smaller channels), TikTok, Amazon Ads and retail/commerce media in general, Linkedin, Twitter/X ads, Pinterest, Snapchat, display ads, video ads, influencer marketing, PR activities, events, TV/CTV, OOH, radio, print advertising, leaflets are examples of channels that are usually
There are some cases where MMM may not be able to accurately measure channel performance so keep these in mind
Existing experiment results can significantly speed up any MMM program and will almost certainly lead to a faster value and ROI on your MMM project.
Set up a good data collection process for areas like promotions (promotion calendars and plans), main events affecting your business, discounting or major pricing changes etc.
MMM can be used to e.g. understand how Youtube or TV ads impact your sales on Amazon, Walmart or other marketplaces. Or how it impacts your sales with retail partners or in your own retail store network – this is one of the very frequent use cases of MMM.
And you run a campaign on Instagram, Youtube, TikTok and CTV – using MMM you can see the overall ROI of these medias for each of the sales channels.
Branding and long-term mean different things for different companies. Generally MMM is quite reliable if the results (sales effect) comes up to 3-4 months after the activity. For this use case standard / modern MMM techniques are suitable and good enough.
Measuring accurately truly long-term effects of marketing where you communicate to potential customers who are not in-market and may not be for many months (and you are trying to mostly build mental availability) is difficult and standard MMM is not suitable for this. There are ways to extend MMM to cover even these use cases – this will inevitably require a much longer time series of data (think 4-5 years instead of 2 years) and additional techniques.
They are not really the focus of MMM but in some cases it may be important for the advertiser to have some insights into them even via MMM – let’s discuss them one by one
Yes these are the typical use cases where MMM is very strong and offers a better solution than most (even data driven) attribution models. As MMM does not need any tracking it is not affected by typical tracking issues or the fact that many formats on these platforms work (influence consumers) without generating clicks.
Yes – this is another good use case for MMM. It requires some specific data preparation but a good MMM solution should have no problem with influencer marketing.
Yes – another typical use case (and totally ignored by attribution models). There are multiple ways to integrate price changes and/or discounts into MMM – each of them having pros and cons. One thing you need to do: collect the necessary data for these in a time series – unless you do so already.
MMM used to be the territory of Fortune 500 brands but with advances over the past cca 5 years, it has become much more accessible. But it is still quite a complex undertaking. Our benchmark is that if your brand spends at least 2 million USD/EUR annually in media (online or offline), MMM should be relevant for you and the insights and optimization should easily offset the complexity and cost of MMM.
For smaller brands it is still possible to implement an MMM solution – there is no hard minimum limit on marketing spend – but it may require a more careful consideration whether it makes sense in terms of your focus and potential business impact.
MMM is relevant for many B2B categories too – e.g. many B2B technology and SaaS companies use it. Depending on the length of your sales cycle you may want to either model actual sales or the number of MQLs or SQLs as the modelled KPI.
There are some industries with very long sales cycles and too complex distribution channel systems that may not be suitable – it is best to discuss your specific case with your MMM partner.
Most brands can benefit from MMM but there are some cases where in our experience either delivering a reliable MMM is often challenging or the business impact may not be material:
There are some standard statistical measures than can be used to assess the model quality, most often used are:
Besides that the best way to develop trust in the model results is to perform tests and compare their actual results with what the model predicts – more info is in the section on model validation
In general there are 2 main ways: